Overview

Dataset statistics

Number of variables29
Number of observations12532
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory225.0 B

Variable types

NUM15
CAT12
BOOL2

Warnings

sewer has a high cardinality: 100 distinct values High cardinality
water has a high cardinality: 73 distinct values High cardinality
app has a high cardinality: 146 distinct values High cardinality
heating has a high cardinality: 89 distinct values High cardinality
cooling has a high cardinality: 115 distinct values High cardinality
materials has a high cardinality: 156 distinct values High cardinality
roof has a high cardinality: 272 distinct values High cardinality
interior has a high cardinality: 439 distinct values High cardinality
bed is highly correlated with bathHigh correlation
bath is highly correlated with bedHigh correlation
total_spaces is highly skewed (γ1 = 60.01606963) Skewed
year is highly skewed (γ1 = 35.77358096) Skewed
df_index has unique values Unique
living has 148 (1.2%) zeros Zeros
lot_a has 809 (6.5%) zeros Zeros
tax_assessed has 537 (4.3%) zeros Zeros
stories has 1356 (10.8%) zeros Zeros

Reproduction

Analysis started2023-02-08 03:05:18.187470
Analysis finished2023-02-08 03:05:36.854358
Duration18.67 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct12532
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6768.654325
Minimum0
Maximum13337
Zeros1
Zeros (%)< 0.1%
Memory size97.9 KiB
2023-02-08T10:05:36.915436image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile718.55
Q13524.75
median6814.5
Q310022.25
95-th percentile12678.9
Maximum13337
Range13337
Interquartile range (IQR)6497.5

Descriptive statistics

Standard deviation3796.310452
Coefficient of variation (CV)0.5608663509
Kurtosis-1.172766567
Mean6768.654325
Median Absolute Deviation (MAD)3249.5
Skewness-0.02047716525
Sum84824776
Variance14411973.05
MonotocityStrictly increasing
2023-02-08T10:05:37.006798image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01< 0.1%
 
88651< 0.1%
 
47431< 0.1%
 
108881< 0.1%
 
88411< 0.1%
 
129391< 0.1%
 
27001< 0.1%
 
67981< 0.1%
 
47511< 0.1%
 
108961< 0.1%
 
Other values (12522)1252299.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
51< 0.1%
 
ValueCountFrequency (%) 
133371< 0.1%
 
133361< 0.1%
 
133351< 0.1%
 
133341< 0.1%
 
133331< 0.1%
 

price
Real number (ℝ≥0)

Distinct2325
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean718536.1905
Minimum0
Maximum4000000
Zeros2
Zeros (%)< 0.1%
Memory size97.9 KiB
2023-02-08T10:05:37.085882image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile165000
Q1299900
median484500
Q3875000
95-th percentile2150000
Maximum4000000
Range4000000
Interquartile range (IQR)575100

Descriptive statistics

Standard deviation654363.5506
Coefficient of variation (CV)0.9106897596
Kurtosis5.214117613
Mean718536.1905
Median Absolute Deviation (MAD)219875
Skewness2.156878253
Sum9004695539
Variance4.281916563e+11
MonotocityNot monotonic
2023-02-08T10:05:37.165260image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3500001190.9%
 
325000840.7%
 
299900840.7%
 
650000800.6%
 
375000790.6%
 
399900780.6%
 
450000760.6%
 
399000740.6%
 
300000730.6%
 
275000720.6%
 
Other values (2315)1171393.5%
 
ValueCountFrequency (%) 
02< 0.1%
 
199001< 0.1%
 
300002< 0.1%
 
329001< 0.1%
 
350001< 0.1%
 
ValueCountFrequency (%) 
40000002< 0.1%
 
39999993< 0.1%
 
39990003< 0.1%
 
39980001< 0.1%
 
3995000120.1%
 

status
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size97.9 KiB
House for sale
9924 
Active
1770 
New construction
 
514
New
 
158
Foreclosure
 
66
Other values (4)
 
100
ValueCountFrequency (%) 
House for sale992479.2%
 
Active177014.1%
 
New construction5144.1%
 
New1581.3%
 
Foreclosure660.5%
 
Price Change450.4%
 
Coming soon410.3%
 
Auction90.1%
 
Re-activated5< 0.1%
 
2023-02-08T10:05:37.240232image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-02-08T10:05:37.282892image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:37.346828image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length14
Mean length12.77481647
Min length3

add_attr
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
True
9551 
False
2981 
ValueCountFrequency (%) 
True955176.2%
 
False298123.8%
 
2023-02-08T10:05:37.383300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

state
Categorical

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size97.9 KiB
TX
3231 
CA
1778 
OH
752 
AZ
751 
IN
739 
Other values (9)
5281 
ValueCountFrequency (%) 
TX323125.8%
 
CA177814.2%
 
OH7526.0%
 
AZ7516.0%
 
IN7395.9%
 
PA7385.9%
 
FL7225.8%
 
NC7155.7%
 
IL7115.7%
 
TN6445.1%
 
Other values (4)175114.0%
 
2023-02-08T10:05:37.427632image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2023-02-08T10:05:37.481174image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

lat
Real number (ℝ≥0)

Distinct12418
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.79885016
Minimum0
Maximum47.733967
Zeros1
Zeros (%)< 0.1%
Memory size97.9 KiB
2023-02-08T10:05:37.542444image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.5874241
Q132.73549675
median35.1891405
Q339.89418175
95-th percentile41.9061163
Maximum47.733967
Range47.733967
Interquartile range (IQR)7.158685

Descriptive statistics

Standard deviation4.442612396
Coefficient of variation (CV)0.1240993042
Kurtosis-0.3488390997
Mean35.79885016
Median Absolute Deviation (MAD)4.5767115
Skewness0.2716118883
Sum448631.1902
Variance19.7368049
MonotocityNot monotonic
2023-02-08T10:05:37.615648image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
40.0150833< 0.1%
 
34.1579442< 0.1%
 
39.6854932< 0.1%
 
39.958622< 0.1%
 
40.0295072< 0.1%
 
32.8155332< 0.1%
 
32.7375532< 0.1%
 
29.6022642< 0.1%
 
39.948282< 0.1%
 
39.9814642< 0.1%
 
Other values (12408)1251199.8%
 
ValueCountFrequency (%) 
01< 0.1%
 
29.1386991< 0.1%
 
29.14871< 0.1%
 
29.1666891< 0.1%
 
29.1986271< 0.1%
 
ValueCountFrequency (%) 
47.7339671< 0.1%
 
47.7331621< 0.1%
 
47.7328071< 0.1%
 
47.7318231< 0.1%
 
47.730671< 0.1%
 

long
Real number (ℝ)

Distinct12392
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-94.85075051
Minimum-122.508575
Maximum0
Zeros1
Zeros (%)< 0.1%
Memory size97.9 KiB
2023-02-08T10:05:37.687968image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-122.508575
5-th percentile-121.9051895
Q1-104.939925
median-95.430715
Q3-82.9066975
95-th percentile-75.0105505
Maximum0
Range122.508575
Interquartile range (IQR)22.0332275

Descriptive statistics

Standard deviation14.76865219
Coefficient of variation (CV)-0.1557041153
Kurtosis-0.8306255116
Mean-94.85075051
Median Absolute Deviation (MAD)12.44971
Skewness-0.4221282019
Sum-1188669.605
Variance218.1130877
MonotocityNot monotonic
2023-02-08T10:05:37.764772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-87.673692< 0.1%
 
-83.004272< 0.1%
 
-82.994442< 0.1%
 
-80.844242< 0.1%
 
-73.801692< 0.1%
 
-97.373652< 0.1%
 
-76.935972< 0.1%
 
-98.6574252< 0.1%
 
-98.658862< 0.1%
 
-96.674442< 0.1%
 
Other values (12382)1251299.8%
 
ValueCountFrequency (%) 
-122.5085751< 0.1%
 
-122.508411< 0.1%
 
-122.507721< 0.1%
 
-122.507591< 0.1%
 
-122.506771< 0.1%
 
ValueCountFrequency (%) 
01< 0.1%
 
-73.70411< 0.1%
 
-73.7045441< 0.1%
 
-73.705731< 0.1%
 
-73.709511< 0.1%
 

bath
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.538142356
Minimum1.5
Maximum5.5
Zeros0
Zeros (%)0.0%
Memory size97.9 KiB
2023-02-08T10:05:37.825289image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum5.5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8839703363
Coefficient of variation (CV)0.2498402403
Kurtosis-0.3372601497
Mean3.538142356
Median Absolute Deviation (MAD)1
Skewness0.2686958455
Sum44340
Variance0.7814035555
MonotocityNot monotonic
2023-02-08T10:05:37.872546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
3541343.2%
 
4422533.7%
 
5132510.6%
 
210618.5%
 
5.54233.4%
 
1.5850.7%
 
ValueCountFrequency (%) 
1.5850.7%
 
210618.5%
 
3541343.2%
 
4422533.7%
 
5132510.6%
 
ValueCountFrequency (%) 
5.54233.4%
 
5132510.6%
 
4422533.7%
 
3541343.2%
 
210618.5%
 

bed
Real number (ℝ≥0)

HIGH CORRELATION

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.563517395
Minimum0
Maximum14
Zeros16
Zeros (%)0.1%
Memory size97.9 KiB
2023-02-08T10:05:37.928334image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9903548966
Coefficient of variation (CV)0.2779149887
Kurtosis4.449020147
Mean3.563517395
Median Absolute Deviation (MAD)1
Skewness0.9733658236
Sum44658
Variance0.9808028213
MonotocityNot monotonic
2023-02-08T10:05:37.988242image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%) 
3541343.2%
 
4422533.7%
 
5132510.6%
 
210618.5%
 
63282.6%
 
1690.6%
 
7630.5%
 
8170.1%
 
0160.1%
 
94< 0.1%
 
Other values (4)110.1%
 
ValueCountFrequency (%) 
0160.1%
 
1690.6%
 
210618.5%
 
3541343.2%
 
4422533.7%
 
ValueCountFrequency (%) 
141< 0.1%
 
122< 0.1%
 
114< 0.1%
 
104< 0.1%
 
94< 0.1%
 

living
Real number (ℝ≥0)

ZEROS

Distinct3510
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2131.440233
Minimum0
Maximum8000
Zeros148
Zeros (%)1.2%
Memory size97.9 KiB
2023-02-08T10:05:38.055135image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile918
Q11384
median1885.5
Q32600
95-th percentile4260.45
Maximum8000
Range8000
Interquartile range (IQR)1216

Descriptive statistics

Standard deviation1087.33886
Coefficient of variation (CV)0.5101427867
Kurtosis3.093274666
Mean2131.440233
Median Absolute Deviation (MAD)576.5
Skewness1.433336968
Sum26711209
Variance1182305.797
MonotocityNot monotonic
2023-02-08T10:05:38.129349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01481.2%
 
1200590.5%
 
1800420.3%
 
2000400.3%
 
3000380.3%
 
1600380.3%
 
1440370.3%
 
1500370.3%
 
2200350.3%
 
2100330.3%
 
Other values (3500)1202596.0%
 
ValueCountFrequency (%) 
01481.2%
 
12< 0.1%
 
3001< 0.1%
 
3921< 0.1%
 
4001< 0.1%
 
ValueCountFrequency (%) 
80002< 0.1%
 
79801< 0.1%
 
79211< 0.1%
 
78151< 0.1%
 
77391< 0.1%
 

lot_a
Real number (ℝ≥0)

ZEROS

Distinct4110
Distinct (%)32.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4301.277688
Minimum0
Maximum10846.44
Zeros809
Zeros (%)6.5%
Memory size97.9 KiB
2023-02-08T10:05:38.207205image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.48
median4647.852
Q36969.6
95-th percentile9583.2
Maximum10846.44
Range10846.44
Interquartile range (IQR)6966.12

Descriptive statistics

Standard deviation3279.932024
Coefficient of variation (CV)0.7625483083
Kurtosis-1.225198237
Mean4301.277688
Median Absolute Deviation (MAD)2757.348
Skewness0.04332859187
Sum53903611.98
Variance10757954.08
MonotocityNot monotonic
2023-02-08T10:05:38.280072image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
08096.5%
 
7405.21411.1%
 
65341391.1%
 
6098.41351.1%
 
4791.61291.0%
 
5227.21231.0%
 
7840.81180.9%
 
43561150.9%
 
8276.41080.9%
 
10018.81080.9%
 
Other values (4100)1060784.6%
 
ValueCountFrequency (%) 
08096.5%
 
0.131< 0.1%
 
0.1461< 0.1%
 
0.1721< 0.1%
 
0.211< 0.1%
 
ValueCountFrequency (%) 
10846.441< 0.1%
 
108461< 0.1%
 
10837.7281< 0.1%
 
10833.3721< 0.1%
 
10815.9481< 0.1%
 

tax_assessed
Real number (ℝ≥0)

ZEROS

Distinct9715
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean394814.7968
Minimum0
Maximum2499000
Zeros537
Zeros (%)4.3%
Memory size97.9 KiB
2023-02-08T10:05:38.353313image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14071.65
Q1155000
median272803.5
Q3513050
95-th percentile1213995.25
Maximum2499000
Range2499000
Interquartile range (IQR)358050

Descriptive statistics

Standard deviation383177.391
Coefficient of variation (CV)0.9705243932
Kurtosis5.34894722
Mean394814.7968
Median Absolute Deviation (MAD)153030.5
Skewness2.093016321
Sum4947819033
Variance1.46824913e+11
MonotocityNot monotonic
2023-02-08T10:05:38.430273image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
05374.3%
 
5000180.1%
 
19500080.1%
 
4500080.1%
 
36000070.1%
 
20500070.1%
 
23000070.1%
 
6100006< 0.1%
 
350006< 0.1%
 
2600006< 0.1%
 
Other values (9705)1192295.1%
 
ValueCountFrequency (%) 
05374.3%
 
1001< 0.1%
 
2001< 0.1%
 
4951< 0.1%
 
7001< 0.1%
 
ValueCountFrequency (%) 
24990001< 0.1%
 
24853301< 0.1%
 
24754211< 0.1%
 
24660631< 0.1%
 
24650601< 0.1%
 

fireplace
Real number (ℝ≥0)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.125279285
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size97.9 KiB
2023-02-08T10:05:38.490419image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5070181404
Coefficient of variation (CV)0.4505709357
Kurtosis46.76316355
Mean1.125279285
Median Absolute Deviation (MAD)0
Skewness5.907319112
Sum14102
Variance0.2570673947
MonotocityNot monotonic
2023-02-08T10:05:38.540641image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
11150791.8%
 
27025.6%
 
31981.6%
 
4650.5%
 
5390.3%
 
6100.1%
 
780.1%
 
92< 0.1%
 
81< 0.1%
 
ValueCountFrequency (%) 
11150791.8%
 
27025.6%
 
31981.6%
 
4650.5%
 
5390.3%
 
ValueCountFrequency (%) 
92< 0.1%
 
81< 0.1%
 
780.1%
 
6100.1%
 
5390.3%
 

parking
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size97.9 KiB
1
12184 
0
 
348
ValueCountFrequency (%) 
11218497.2%
 
03482.8%
 
2023-02-08T10:05:38.580875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

covered
Real number (ℝ≥0)

Distinct291
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.924721884
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size97.9 KiB
2023-02-08T10:05:38.628849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.76
median2
Q32
95-th percentile3
Maximum5
Range4
Interquartile range (IQR)0.24

Descriptive statistics

Standard deviation0.5606160541
Coefficient of variation (CV)0.2912712007
Kurtosis5.649138899
Mean1.924721884
Median Absolute Deviation (MAD)0.045
Skewness1.368775142
Sum24120.61465
Variance0.3142903601
MonotocityNot monotonic
2023-02-08T10:05:38.702588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2557544.5%
 
1160912.8%
 
37796.2%
 
42071.7%
 
1.721090.9%
 
1.75930.7%
 
1.82920.7%
 
1.65910.7%
 
1.975900.7%
 
1.995880.7%
 
Other values (281)379930.3%
 
ValueCountFrequency (%) 
1160912.8%
 
1.0451< 0.1%
 
1.0853< 0.1%
 
1.1351< 0.1%
 
1.144< 0.1%
 
ValueCountFrequency (%) 
5510.4%
 
42071.7%
 
3.53< 0.1%
 
3.341< 0.1%
 
3.1451< 0.1%
 

garage
Real number (ℝ≥0)

Distinct240
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.876448182
Minimum1.3125
Maximum2.4125
Zeros0
Zeros (%)0.0%
Memory size97.9 KiB
2023-02-08T10:05:38.779136image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1.3125
5-th percentile1.3125
Q11.725
median2
Q32
95-th percentile2.4125
Maximum2.4125
Range1.1
Interquartile range (IQR)0.275

Descriptive statistics

Standard deviation0.2956233863
Coefficient of variation (CV)0.1575441247
Kurtosis-0.09521204028
Mean1.876448182
Median Absolute Deviation (MAD)0.055
Skewness-0.4949913579
Sum23515.64861
Variance0.08739318652
MonotocityNot monotonic
2023-02-08T10:05:38.848811image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2552744.1%
 
1.3125165413.2%
 
2.412510678.5%
 
1.6851090.9%
 
1.6251030.8%
 
1.945990.8%
 
1.985970.8%
 
1.78910.7%
 
1.54900.7%
 
1.925880.7%
 
Other values (230)360728.8%
 
ValueCountFrequency (%) 
1.3125165413.2%
 
1.3153< 0.1%
 
1.32130.1%
 
1.331< 0.1%
 
1.3356< 0.1%
 
ValueCountFrequency (%) 
2.412510678.5%
 
2.4051< 0.1%
 
2.3952< 0.1%
 
2.381< 0.1%
 
2.3751< 0.1%
 

total_spaces
Real number (ℝ≥0)

SKEWED

Distinct296
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.345138771
Minimum1
Maximum212
Zeros0
Zeros (%)0.0%
Memory size97.9 KiB
2023-02-08T10:05:38.921637image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32.32
95-th percentile4
Maximum212
Range211
Interquartile range (IQR)0.32

Descriptive statistics

Standard deviation2.848027562
Coefficient of variation (CV)1.21443882
Kurtosis4309.804419
Mean2.345138771
Median Absolute Deviation (MAD)0.095
Skewness60.01606963
Sum29389.27908
Variance8.111260992
MonotocityNot monotonic
2023-02-08T10:05:38.991570image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2538443.0%
 
1163313.0%
 
39307.4%
 
48196.5%
 
62071.7%
 
51811.4%
 
2.1751241.0%
 
2.01730.6%
 
2.52710.6%
 
1.85640.5%
 
Other values (286)304624.3%
 
ValueCountFrequency (%) 
1163313.0%
 
1.3551< 0.1%
 
1.3953< 0.1%
 
1.471< 0.1%
 
1.4856< 0.1%
 
ValueCountFrequency (%) 
2121< 0.1%
 
2031< 0.1%
 
251< 0.1%
 
211< 0.1%
 
201< 0.1%
 

subtype
Categorical

Distinct26
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size97.9 KiB
Single Family Residence
10293 
Residentia
 
974
Detached
 
459
none
 
322
Residential
 
202
Other values (21)
 
282
ValueCountFrequency (%) 
Single Family Residence1029382.1%
 
Residentia9747.8%
 
Detached4593.7%
 
none3222.6%
 
Residential2021.6%
 
Single Family - Detached1160.9%
 
Ranch410.3%
 
All Other Attached330.3%
 
Single Family - Semi-Attached230.2%
 
Residential-Detache130.1%
 
Other values (16)560.4%
 
2023-02-08T10:05:39.282547image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5 ?
Unique (%)< 0.1%
2023-02-08T10:05:39.344339image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length29
Median length23
Mean length20.67299713
Min length4

year
Real number (ℝ≥0)

SKEWED

Distinct196
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1965.777867
Minimum0
Maximum9999
Zeros15
Zeros (%)0.1%
Memory size97.9 KiB
2023-02-08T10:05:39.409682image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1906
Q11941
median1966
Q31998
95-th percentile2022
Maximum9999
Range9999
Interquartile range (IQR)57

Descriptive statistics

Standard deviation127.2147766
Coefficient of variation (CV)0.06471472628
Kurtosis2604.285844
Mean1965.777867
Median Absolute Deviation (MAD)28
Skewness35.77358096
Sum24635128.22
Variance16183.59938
MonotocityNot monotonic
2023-02-08T10:05:39.482778image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20226074.8%
 
19253232.6%
 
19502672.1%
 
19202441.9%
 
19552231.8%
 
19002181.7%
 
19402021.6%
 
20062021.6%
 
20051901.5%
 
19601891.5%
 
Other values (186)986778.7%
 
ValueCountFrequency (%) 
0150.1%
 
17301< 0.1%
 
17501< 0.1%
 
18071< 0.1%
 
18301< 0.1%
 
ValueCountFrequency (%) 
99992< 0.1%
 
2023640.5%
 
20226074.8%
 
2021.9570.1%
 
2021680.5%
 

sewer
Categorical

HIGH CARDINALITY

Distinct100
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size97.9 KiB
Public Sewer
6954 
none
2245 
City Sewer
1364 
Sewer Connecte
 
430
SAW
 
201
Other values (95)
1338 
ValueCountFrequency (%) 
Public Sewer695455.5%
 
none224517.9%
 
City Sewer136410.9%
 
Sewer Connecte4303.4%
 
SAW2011.6%
 
Sewer System1801.4%
 
Septic System1170.9%
 
Septic Tank1130.9%
 
Sewer Connected1020.8%
 
SAWS1010.8%
 
Other values (90)7255.8%
 
2023-02-08T10:05:39.563012image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique29 ?
Unique (%)0.2%
2023-02-08T10:05:39.638344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length32
Median length12
Mean length10.23068944
Min length1

water
Categorical

HIGH CARDINALITY

Distinct73
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size97.9 KiB
Public
6599 
none
1896 
City Water
1049 
Publi
 
530
Lake Michigan
 
363
Other values (68)
2095 
ValueCountFrequency (%) 
Public659952.7%
 
none189615.1%
 
City Water10498.4%
 
Publi5304.2%
 
Lake Michigan3632.9%
 
Meter on Property3482.8%
 
City Wate3062.4%
 
SAW2622.1%
 
Water System1991.6%
 
Water District1591.3%
 
Other values (63)8216.6%
 
2023-02-08T10:05:39.715368image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique25 ?
Unique (%)0.2%
2023-02-08T10:05:39.786584image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length32
Median length6
Mean length7.038860517
Min length3

app
Categorical

HIGH CARDINALITY

Distinct146
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size97.9 KiB
dishwasher
4734 
none
1669 
electric water heater
542 
gas water heater
537 
built-in microwave
529 
Other values (141)
4521 
ValueCountFrequency (%) 
dishwasher473437.8%
 
none166913.3%
 
electric water heater5424.3%
 
gas water heater5374.3%
 
built-in microwave5294.2%
 
range5154.1%
 
cooktop2962.4%
 
electric cooktop2842.3%
 
gas cooktop2562.0%
 
dryer2421.9%
 
Other values (136)292823.4%
 
2023-02-08T10:05:39.858751image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique36 ?
Unique (%)0.3%
2023-02-08T10:05:39.930912image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length38
Median length10
Mean length10.92148101
Min length3

heating
Categorical

HIGH CARDINALITY

Distinct89
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size97.9 KiB
central
4421 
forced air
2737 
natural gas
1904 
electric
852 
none
627 
Other values (84)
1991 
ValueCountFrequency (%) 
central442135.3%
 
forced air273721.8%
 
natural gas190415.2%
 
electric8526.8%
 
none6275.0%
 
hot water2612.1%
 
fireplace(s1521.2%
 
radiator1411.1%
 
central forced air1251.0%
 
other1170.9%
 
Other values (79)11959.5%
 
2023-02-08T10:05:40.007325image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique21 ?
Unique (%)0.2%
2023-02-08T10:05:40.076890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length31
Median length8
Mean length8.913501436
Min length2

cooling
Categorical

HIGH CARDINALITY

Distinct115
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size97.9 KiB
Central Air
4531 
Ceiling Fan(s
1476 
Central Ai
1127 
None
790 
none
694 
Other values (110)
3914 
ValueCountFrequency (%) 
Central Air453136.2%
 
Ceiling Fan(s147611.8%
 
Central Ai11279.0%
 
None7906.3%
 
none6945.5%
 
Electri5034.0%
 
Central A/5004.0%
 
Electric3883.1%
 
Central Forced Air2752.2%
 
Refrigeration2101.7%
 
Other values (105)203816.3%
 
2023-02-08T10:05:40.148287image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique23 ?
Unique (%)0.2%
2023-02-08T10:05:40.218257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length31
Median length11
Mean length10.39594638
Min length2

stories
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.488788701
Minimum0
Maximum7
Zeros1356
Zeros (%)10.8%
Memory size97.9 KiB
2023-02-08T10:05:40.272082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8473448893
Coefficient of variation (CV)0.5691505375
Kurtosis0.1737491559
Mean1.488788701
Median Absolute Deviation (MAD)1
Skewness0.258347578
Sum18657.5
Variance0.7179933615
MonotocityNot monotonic
2023-02-08T10:05:40.320745image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
1511440.8%
 
2471437.6%
 
0135610.8%
 
310168.1%
 
3.51351.1%
 
2.5910.7%
 
4730.6%
 
1.5270.2%
 
53< 0.1%
 
72< 0.1%
 
ValueCountFrequency (%) 
0135610.8%
 
1511440.8%
 
1.5270.2%
 
2471437.6%
 
2.5910.7%
 
ValueCountFrequency (%) 
72< 0.1%
 
61< 0.1%
 
53< 0.1%
 
4730.6%
 
3.51351.1%
 

materials
Categorical

HIGH CARDINALITY

Distinct156
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size97.9 KiB
brick
3996 
none
1793 
stucco
721 
vinyl sideing
618 
frame
508 
Other values (151)
4896 
ValueCountFrequency (%) 
brick399631.9%
 
none179314.3%
 
stucco7215.8%
 
vinyl sideing6184.9%
 
frame5084.1%
 
masonry4693.7%
 
wood sideing3923.1%
 
frame - woo3743.0%
 
stone3472.8%
 
block3312.6%
 
Other values (146)298323.8%
 
2023-02-08T10:05:40.396306image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique45 ?
Unique (%)0.4%
2023-02-08T10:05:40.470400image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length5
Mean length7.320699011
Min length3

roof
Categorical

HIGH CARDINALITY

Distinct272
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size97.9 KiB
none
4058 
Composition
3980 
Shingle
1308 
Tile
519 
Asphalt
431 
Other values (267)
2236 
ValueCountFrequency (%) 
none405832.4%
 
Composition398031.8%
 
Shingle130810.4%
 
Tile5194.1%
 
Asphalt4313.4%
 
Comp Shingle2812.2%
 
Metal1821.5%
 
Other1721.4%
 
Composition,Shingle1401.1%
 
Flat1391.1%
 
Other values (262)132210.5%
 
2023-02-08T10:05:40.546647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique138 ?
Unique (%)1.1%
2023-02-08T10:05:40.625266image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length57
Median length7
Mean length8.150734121
Min length4

foundation
Categorical

Distinct49
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size97.9 KiB
none
5388 
slab
3543 
concrete perimeter
676 
crawl space
 
462
pillar/post/pier
 
462
Other values (44)
2001 
ValueCountFrequency (%) 
none538843.0%
 
slab354328.3%
 
concrete perimeter6765.4%
 
crawl space4623.7%
 
pillar/post/pier4623.7%
 
poured concrete4413.5%
 
blogck3062.4%
 
other2231.8%
 
stone1591.3%
 
brick/mortar1571.3%
 
Other values (39)7155.7%
 
2023-02-08T10:05:40.702170image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique11 ?
Unique (%)0.1%
2023-02-08T10:05:40.768921image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length27
Median length4
Mean length6.444861155
Min length4

interior
Categorical

HIGH CARDINALITY

Distinct439
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size97.9 KiB
none
3735 
Ceiling Fan(s
 
559
Cable TV Availabl
 
553
Built-in Feature
 
515
One Living Are
 
358
Other values (434)
6812 
ValueCountFrequency (%) 
none373529.8%
 
Ceiling Fan(s5594.5%
 
Cable TV Availabl5534.4%
 
Built-in Feature5154.1%
 
One Living Are3582.9%
 
Walk-In Closet(s3162.5%
 
Breakfast Ba2431.9%
 
Two Living Are2391.9%
 
High Ceiling2271.8%
 
Walk-In Closet(s)2191.7%
 
Other values (429)556844.4%
 
2023-02-08T10:05:40.843047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique186 ?
Unique (%)1.5%
2023-02-08T10:05:40.921218image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length38
Median length13
Mean length11.40025535
Min length2

Interactions

2023-02-08T10:05:20.414601image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:20.488338image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:20.562441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:20.627038image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:20.691236image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:20.798014image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:20.866046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:20.938618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:21.001094image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:21.065754image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:21.131737image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:21.204322image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:21.273843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:21.343671image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:21.405034image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:21.487862image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:21.559506image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:21.628812image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:21.694950image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:21.760349image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:21.828853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:21.894843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:21.963738image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:22.028858image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:22.100748image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:22.169696image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:22.240067image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:22.305747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:22.370384image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:22.436160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:22.502788image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:22.564615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:22.641798image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:22.699110image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:22.758540image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:22.820272image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:22.879445image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:22.942145image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:23.001986image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:23.061706image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:23.123563image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:23.185304image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:23.242623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:23.299253image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:23.356841image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:23.415695image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:23.478522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:24.125361image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:24.184495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:24.244947image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:24.309232image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:24.370504image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:24.434312image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:24.494068image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:24.559122image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:24.623184image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:24.687367image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:24.747777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:24.806773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:24.867360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:24.929122image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:24.996116image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:25.065899image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:25.129670image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:25.195832image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:25.263959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:25.330005image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:25.398621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:25.463249image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:25.529978image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:25.597824image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:25.665722image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:25.749971image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:25.817710image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:25.882730image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:25.948817image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:26.012817image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:26.077158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:26.136891image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:26.198806image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:26.263029image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:26.324737image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:26.395470image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:26.460981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:26.525278image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:26.598197image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:26.673974image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:26.737306image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:26.913549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:26.987648image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:27.057438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:27.150675image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:27.222441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:27.288102image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:27.353694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:27.422255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:27.489317image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:27.557758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:27.622776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:27.689139image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:27.756744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:27.826338image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:27.892063image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:27.955404image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:28.020350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:28.086027image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:28.151956image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:28.216926image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:28.276238image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:28.336868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:28.400754image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:28.464468image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:28.528433image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:28.587244image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:28.649970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:28.715248image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:28.779135image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:28.838827image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:28.897022image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:28.955862image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:29.016181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:29.081576image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:29.146252image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:29.206183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:29.290285image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:29.355770image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:29.416901image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:29.480420image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:29.539868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:29.600251image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:29.663072image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:29.726072image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:29.787247image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:29.845086image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:29.913886image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:29.975002image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:30.041922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:30.109895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:30.173747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:30.368285image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:30.435541image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:30.500770image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:30.568834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:30.633279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:30.699167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:30.766117image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:30.834102image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:30.898085image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:30.961780image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:31.025261image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:31.089577image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:31.158434image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:31.230652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:31.313761image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:31.384435image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:31.455681image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:31.547842image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:31.617393image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:31.682188image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:31.748403image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:31.816159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:31.883774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:31.954199image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:32.016654image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:32.080311image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:32.146236image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:32.209720image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:32.272657image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:32.330225image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:32.390076image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:32.453161image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:32.520744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:32.589471image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:32.649008image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:32.710450image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:32.772478image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:32.835132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:32.893604image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:32.951028image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:33.009467image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:33.069376image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:33.130077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:33.190089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:33.245339image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:33.302305image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:33.362606image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:33.420446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:33.483633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:33.540452image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:33.600865image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:33.671594image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:33.747466image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:33.804286image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:33.859223image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:33.916446image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:33.975655image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:34.037428image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:34.100409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:34.182891image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:34.252482image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:34.316952image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:34.377379image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:34.634506image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:34.695444image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:34.755138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:34.816151image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:34.879284image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:34.938278image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:34.995644image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:35.068533image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:35.131908image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:35.213548image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:35.300176image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:35.363094image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:35.427313image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:35.494001image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:35.558589image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:35.627416image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:35.690674image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:35.774044image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:35.845330image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:35.913810image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:35.981214image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:36.061302image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:36.124694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2023-02-08T10:05:40.989700image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-02-08T10:05:41.102210image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-02-08T10:05:41.210539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-02-08T10:05:41.331385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2023-02-08T10:05:41.447931image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2023-02-08T10:05:36.312030image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2023-02-08T10:05:36.721624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Sample

First rows

df_indexpricestatusadd_attrstatelatlongbathbedlivinglot_atax_assessedfireplaceparkingcoveredgaragetotal_spacessubtypeyearsewerwaterappheatingcoolingstoriesmaterialsrooffoundationinterior
00274000House for saleTrueNY40.675730-73.8223503.0315562400.001.01.01.7501.59501.945none1930.0nonenonenonenonenone0.0nonenonenonenone
11270000House for saleTrueNY40.670036-73.7804504.0419203998.06780001.01.01.6601.53502.160none1950.0nonenonemicrowavenonenone0.0nonenonenonenone
22899000House for saleTrueNY40.524227-74.2157903.0325326903.06370001.01.01.9352.00002.000Single Family - Detached1899.0Public Sewernonedishwasherhot waterUnits2.0nonenonenonenone
331390000House for saleTrueNY40.721615-73.8207554.0419152697.08940001.01.01.6951.65503.915none1945.0nonenonedryernonenone0.0nonenonenonenone
451380000House for saleTrueNY40.604470-73.9439603.0318002000.001.01.01.7001.31252.160Single Family Residence1930.0Public SewerPublicdishwashernatural gasWall Unit(s)2.0bricknonenoneFormal Dining Roo
56599000House for saleTrueNY40.639286-73.9412703.0313441950.05340001.01.01.8001.68502.520none1925.0nonenonedryernonenone0.0nonenonenonenone
671280000House for saleTrueNY40.622246-74.0178303.0314222613.08820001.01.01.6051.53501.000none1920.0nonenonedishwasherradianCentral0.0noneShake / Shinglenonenone
79899000House for saleTrueNY40.578552-74.0051964.0428003000.04190001.01.01.7201.67002.000none1945.0nonenonedishwashernonenone0.0nonenonenonenone
810565000House for saleTrueNY40.682640-73.7881803.0318961951.04060001.01.01.8001.68502.520none1925.0nonenonedishwashernonenone0.0noneShake / Shinglenonenone
91199999House for saleTrueNY40.627700-74.1790202.028402100.001.01.02.1402.15003.000Single Family Residence1998.0nonenonenonenatural gasnone1.0aluminum sideinFlat,Metalothernone

Last rows

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1252213328299900House for saleTrueTN36.258205-86.6837843.0318888276.401819001.01.01.8551.8802.145Single Family Residence1918.0Public SewerPublicgas water heaterhot waterCentral A/4.0stuccononeconcrete perimeterKitchen - Gourme
1252313329385000House for saleTrueTN36.158485-86.8282403.0311000.252700001.01.01.8501.8502.000Single Family Residence1925.0Public SewerPublicgas water heaterforced airCentral A/3.0aluminum sideinnoneothernone
1252413330599000House for saleTrueTN36.104990-86.7457603.0325328712.002802001.01.01.9651.9552.395Single Family Residence1976.0Public SewerPublicelectric water heaterforced airCentral A/2.0bricknonebrick/mortarnone
1252513331299000House for saleTrueTN36.219044-86.7567603.039600.411533001.00.01.9151.7402.135Single Family Residence1947.0Public SewerPublicoven/range - gashot waterCentral A/3.0brickWood,Otherslabnone
12526133321149900House for saleTrueTN36.193188-86.7778304.042012871.2001.01.01.9201.7802.560Single Family Residence1936.0Public SewerPublicgas water heaterforced airNone3.0bricknonebrick/mortarDining Are
12527133333675000House for saleTrueTN36.123570-86.7955805.0557040.299618001.01.01.9051.8852.000Single Family Residence1953.0Public SewerPublicbuilt-in microwavecentralCentral A/3.0brickFlatslabDining Are
1252813334750000House for saleTrueTN36.160694-86.8478303.0322084356.005639001.01.01.5701.6902.030Single Family Residence1951.0Public SewerPublicgas water heaterforced airCentral A/3.0bricknoneslabnone
1252913335575000House for saleTrueTN36.204834-86.7386604.0436589147.6001.01.01.8051.7802.000Single Family Residence1940.0Public SewerPublicdishwasherforced airCentral A/3.0bricknoneslabKitchen - Gourme
1253013336609000House for saleTrueTN36.196106-86.7351153.031950871.2001.01.01.8001.6852.520Single Family Residence1925.0Public SewerPublicgas water heaterhot waterDuctless/Mini-Spli3.0bricknonebrick/mortarCeiling Fan(s)
1253113337460000Coming soonTrueTN36.026558-86.7177103.0322247840.803196002.01.01.8951.8252.090Single Family Residence1937.0Public SewerPublicbuilt-in microwaveforced airCentral A/3.0combinationUnknownotherCeiling Fan(s